SoroushVahidi/scidocs huggingface dataset:
Support for vehicle-to-everything services based on LTE
(2016)

Vehicular applications and communications technologies are often referred to as vehicle-to-everything (V2X), which is classified into four different types: vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-network (V2N), and vehicle-to-pedestrian (V2P) [1]. V2X related research projects, field tests, and regulatory work have been promoted in different countries and regions. In May 2015, the Ministry of Industry and Information Technology (MIIT) of China explained national strategies, "Made in China 2025," about intelligent connected vehicles. In 2020 and 2025, the overall technology and key technologies for intelligent driver assistance and automatic driving will be available in China, respectively [2]. V2X solutions are the critical technologies to support the realization of such visions. Although IEEE 802.11p has been selected as the technology for V2X communications in some countries such as the United States and in Europe, the intrinsic characteristics of IEEE 802.11p have confined the technology to support low latency with high reliability [3, 4]. Standardization of Long Term Evolution (LTE)-based V2X is being actively conducted by the Third Generation Partnership Project (3GPP) to provide the solution for V2X communications that benefit from the global deployment and fast commercialization of LTE systems. Because of the wide deployment of LTE networks, V2I and V2N services can be provided with high data rate, comprehensive quality of service (QoS) support, ubiquitous coverage, and high penetration rate [5]. Meanwhile, LTE can be extended to support V2V direct communications based on device-to-device (D2D) sidelink design to satisfy the QoS requirements, such as low latency, high reliability, and high speed in the case of high vehicle density [6].
LTE evolution for vehicle-to-everything services
(2016)

Wireless communication has become a key technology for competitiveness of next generation vehicles. Recently, the 3GPP has initiated standardization activities for LTE-based V2X services composed of vehicle-to-vehicle, vehicle- to-pedestrian, and vehicle-to-infrastructure/network. The goal of these 3GPP activities is to enhance LTE systems to enable vehicles to communicate with other vehicles, pedestrians, and infrastructure in order to exchange messages for aiding in road safety, controlling traffic flow, and providing various traffic notifications. In this article, we provide an overview of the service flow and requirements of the V2X services LTE systems are targeting. This article also discusses the scenarios suitable for operating LTE-based V2X services, and addresses the main challenges of high mobility and densely populated vehicle environments in designing technical solutions to fulfill the requirements of V2X services. Leveraging the spectral-efficient air interface, the cost-effective network deployment, and the versatile nature of supporting different communication types, LTE systems along with proper enhancements can be the key enabler of V2X services.
An improved IEEE 802.16 WiMAX module for the ns-3 simulator
(2010)

IEEE 802.16 WiMAX is a promising new wireless technology for providing broadband ubiquitous network access. As more and more researchers and industrials are interested in simulating such networks, a number of WiMAX simulators have been emerged in the networking community. One of the most recent WiMAX simulator is the one developed for ns-3. This module provides a standard compliant and well designed implementation of the standard and benefits from the major enhancements provided by ns-3 (compared to other network simulators) which has all the capabilities of becoming the leading network simulator in near future. However, this WiMAX module still lacks some important features which motivated this work. In this paper, we first provide a snapshot of existing WiMAX simulators available in the public domain, while highlighting their limitations. Then, we describe the new features and enhancements we have integrated within the ns-3 WiMAX module, and in particular: a realistic and scalable physical model, an IP packet classifier for the convergence sub-layer, efficient uplink and downlink schedulers, support for multicast traffic and pcap packet tracing functionality. The new design of the physical layer has improved the simulation time by several magnitude orders while still providing a realistic implementation of the standard. Furthermore, the IP classifier has enabled the simulation of an unlimited number of service flows per subscriber station, while the proposed schedulers improve the management of the QoS requirements for the different service flows.
Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer
(2002)

Objective: To evaluate the use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer.Design: An observational study assessing the nutritional status of patients with cancer.Setting: Oncology ward of a private tertiary Australian hospital.Subjects: Seventy-one cancer patients aged 18–92 y.Intervention: Scored PG-SGA questionnaire, comparison of scored PG-SGA with subjective global assessment (SGA), sensitivity, specificity.Results: Some 24% (17) of 71 patients were well nourished, 59% (42) of patients were moderately or suspected of being malnourished and 17% (12) of patients were severely malnourished according to subjective global assessment (SGA). The PG-SGA score had a sensitivity of 98% and a specificity of 82% at predicting SGA classification. There was a significant difference in the median PG-SGA scores for each of the SGA classifications (P<0.001), with the severely malnourished patients having the highest scores. Re-admission within 30 days of discharge was significantly different between SGA groups (P=0.037). The mortality rate within 30 days of discharge was not significantly different between SGA groups (P=0.305). The median length of stay of well nourished patients (SGA A) was significantly lower than that of the malnourished (SGA B+C) patients (P=0.024).Conclusion: The scored PG-SGA is an easy to use nutrition assessment tool that allows quick identification and prioritisation of malnutrition in hospitalised patients with cancer.Sponsors: The Wesley Research Institute.
Grid Cells and Neural Coding in High-End Cortices
(2013)

An ultimate goal of neuroscience is to understand the mechanisms of mammalian intellectual functions, many of which are thought to depend extensively on the cerebral cortex. While this may have been considered a remote objective when Neuron was launched in 1988, neuroscience has now evolved to a stage where it is possible to decipher neural-circuit mechanisms in the deepest parts of the cortex, far away from sensory receptors and motoneurons. In this review, we show how studies of place cells in the hippocampus and grid cells in the entorhinal cortex may provide some of the first glimpses into these mechanisms. We shall review the events that led up to the discovery of grid cells and a functional circuit in the entorhinal cortex and highlight what we currently see as the big questions in this field--questions that, if resolved, will add to our understanding of cortical computation in a general sense.
A Miniaturized, Dual-Band, Circularly Polarized Microstrip Antenna for Installation Into Satellite Mobile Phones
(2009)

A miniaturized, dual-band, circularly polarized microstrip antenna (CPMA) for satellite mobile phones was designed. Two miniaturized radiation elements using folded structures are stacked vertically to achieve dual bands. Each radiation element shows left-handed circular polarization. The total volume of this dual-band, circularly polarized, microstrip antenna is 46 mm (length) x 24 mm (width) x 13 mm (height). The overall antenna is mounted on a phone-shaped ground plane to fit inside satellite mobile phones.
Learning Deep Features for DNA Methylation Data Analysis
(2016)

Many studies demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. In order to explicitly capture the properties of the data, we design a deep neural network, which composes of several stacked binary restricted Boltzmann machines, to learn the low-dimensional deep features of the DNA methylation data. Experimental results show that these features perform best in breast cancer DNA methylation data cluster analysis, compared with some state-of-the-art methods.
SeriesNet:A Generative Time Series Forecasting Model
(2018)

Time series forecasting is emerging as one of the most important branches of big data analysis. However, traditional time series forecasting models can not effectively extract good enough sequence data features and often result in poor forecasting accuracy. In this paper, a novel time series forecasting model, named SeriesNet, which can fully learn features of time series data in different interval lengths. The SeriesNet consists of two networks. The LSTM network aims to learn holistic features and to reduce dimensionality of multi-conditional data, and the dilated causal convolution network aims to learn different time interval. This model can learn multi-range and multi-level features from time series data, and has higher predictive accuracy compared those models using fixed time intervals. Moreover, this model adopts residual learning and batch normalization to improve generalization. Experimental results show our model has higher forecasting accuracy and has greater stableness on several typical time series data sets.
Discrete Dynamic Shortest Path Problems in Transportation Applications: Complexity and Algorithms with Optimal Run Time
(1998)

This paper solves what appears to be a 30 years old problem dealing with the discovery of most effi algorithms possible to compute all-to-one shortest paths in discrete dynamic networks. This problem lies at th of efficient solution approaches to dynamic network models that arise in dynamic transportation systems, suc Intelligent Transportation Systems (ITS) applications. While the main objective of this paper is the study of the one dynamic shortest paths problem, one-to-all fastest paths problems are studied as well. Early results are and new properties are established. We establish the exact complexity of these problems and develop optima run time sense, solution algorithms. A new and simple solution algorithm is proposed for all-to-one all departur intervals shortest path problems. It is proved, theoretically, that the new solution algorithm has an optimal run complexity that equals the complexity of the problem. Computer implementations and experimental evaluatio various solution algorithms support the theoretical findings and demonstrate the efficiency of the proposed so algorithm. We expect our findings to be of major benefit to research and development activities in the field of dynamic, in particular real-time, management and control of large-scale Intelligent Transportation Systems (I
Developing an Air Hockey Game in LabVIEW
(2017)

Air Hockey consists of two players volleying a puck, using mallets, across a low friction table surface. Points are scored when a player lands the puck in the opponent's goal. The game is won by the player with the higher score at the end of the match. Virtual versions of air hockey have been implemented using various game development software. This paper details the implementation of the air hockey game in LabVIEW. This version of the game uses a physics engine and an Artificial Intelligence (AI) player. This effort discusses the steps followed to design the game and the effectiveness of the AI player versus a human player.
Social Capital and Knowledge Integration in Digitally Enabled Teams
(2008)

T understand the impact of social capital on knowledge integration and performance within digitally enabled teams, we studied 46 teams who had a history and a future working together. All three dimensions of their social capital (structural, relational, and cognitive) were measured prior to the team performing two tasks in a controlled setting, one face-to-face and the other through a lean digital network. Structural and cognitive capital were more important to knowledge integration when teams communicated through lean digital networks than when they communicated face-to-face; relational capital directly impacted knowledge integration equally, regardless of the communication media used by the team. Knowledge integration, in turn, impacted team decision quality, suggesting that social capital influences team performance in part by increasing a team’s ability to integrate knowledge. These results suggest that team history may be necessary but not sufficient for teams to overcome the problems with the use of lean digital networks as a communication environment. However, team history may present a window of opportunity for social capital to develop, which in turn allows teams to perform just as well as in either communication environment.
Graphene-SGX: A Practical Library OS for Unmodified Applications on SGX
(2017)

Intel SGX hardware enables applications to protect themselves from potentially-malicious OSes or hypervisors. In cloud computing and other systems, many users and applications could benefit from SGX. Unfortunately, current applications will not work out-of-the-box on SGX. Although previous work has shown that a library OS can execute unmodified applications on SGX, a belief has developed that a library OS will be ruinous for performance and TCB size, making application code modification an implicit prerequisite to adopting SGX. This paper demonstrates that these concerns are exaggerated, and that a fully-featured library OS can rapidly deploy unmodified applications on SGX with overheads comparable to applications modified to use “shim” layers. We present a port of Graphene to SGX, as well as a number of improvements to make the security benefits of SGX more usable, such as integrity support for dynamically-loaded libraries, and secure multi-process support. Graphene-SGX supports a wide range of unmodified applications, including Apache, GCC, and the R interpreter. The performance overheads of GrapheneSGX range from matching a Linux process to less than 2× in most single-process cases; these overheads are largely attributable to current SGX hardware or missed opportunities to optimize Graphene internals, and are not necessarily fundamental to leaving the application unmodified. Graphene-SGX is open-source and has been used concurrently by other groups for SGX research.
High-Speed Flight of Quadrotor Despite Loss of Single Rotor
(2018)

In order to achieve high-speed flight of a damaged quadrotor with complete loss of a single rotor, a multiloop hybrid nonlinear controller is designed. By fully making use of sensor measurements, the model dependence of this control method is reduced, which is conducive to handling disturbance from the unknown aerodynamic effects. This controller is tested on a quadrotor vehicle with one rotor completely removed in the high-speed condition. Free flights are performed in the Open Jet Facility, a large-scale wind tunnel. Over 9 m/s flight speed is reached for the damaged quadrotor in these tests. In addition, several high-speed spin-induced aerodynamic effects are discovered and analyzed.
Sensor Systems for Prognostics and Health Management
(2010)

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.
Detection and Localization of Texture-less Objects with Deep Neural Networks
(2017)

This thesis studies Faster R-CNN, the state-of-art method for object detection in RGB images, and proposes its extension to RGB-D images. Solutions to the following problems are proposed and evaluated: filling missing values in depth images, depth encoding (raw depth vs. surface normals), extension of the CNN architecture to accept the extra depth information, and initialization of weights in the extended network. The overall best results were achieved with a network that accepts an extra depth channel, pre-processed by the iterative median filter to fill in the missing values, and has the depth weights in the first convolutional layer initialized with the mean of the color weights that were pretrained on ImageNet. However, the improvement over the original method using only RGB channels is not significant (mAP was increased by 1 − 2%), which suggests a need for different incorporation of the depth information.
A Fast Iterative Method for Solving the Eikonal Equation on Triangulated Surfaces
(2011)

This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton-Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput., 30 (2008), pp. 2512-2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.
OntoNotes: The 90% Solution
(2009)

OntoNotes is a five year multi-site collaboration between BBN Technologies, Information Sciences Institute of University of Southern California, University of Colorado, University of Pennsylvania and Brandeis University. The goal of the OntoNotes project is to provide linguistic data annotated with a skeletal representation of the literal meaning of sentences including syntactic parse, predicate-argument structure, coreference, and word senses linked to an ontology, allowing a new generation of language understanding technologies to be developed with new functional capabilities. In its third year of existence, the OntoNotes project has generated a large amount of high quality data covering various layers of linguistic annotation. This is probably the first time that data of such quality has been available in large quantities covering multiple genres (newswire, broadcast news, broadcast conversation and weblogs) and languages (English, Chinese and Arabic). The guiding principle has been to find a “sweet spot” in the space of inter-tagger agreement, productivity, and depth of representation. The most effective use of this resource for research requires simultaneous access to multiple layers of annotation. This has been made possible by representing the corpus with a relational database to accommodate the dense connectedness of the data and ensure consistency across layers. In order to facilitate ease of understanding and manipulability, the database has also been supplemented with a object-oriented Python API. The tutorial consists of two parts. In the first part we will familiarize the user with this new resource, describe the various layers of annotations in some detail and discuss the linguistic principles and sometimes practical considerations behind the important design decisions that shapes the corpus. We will also describe the salient differences between the three languages at each layer of annotation and how linguistic peculiarities of different languages were handled in the data. In the second part, we will describe the data formats of each of the layers and talk about various design decisions that went into the creation of the architecture of the database and the individual tables comprising it, along with issues that came up during the representation process and compromises that were made without sacrificing some primary objectives one of which being the independent existence of each layer that is necessary to allow multi-site collaboration. We will explain how the database schema attempts to interconnect all the layers. Then we will go into the details of the Python API that allows easy access to each of the layers and show that by making the objects closely resemble database tables, the API allows for their flexible integration. This will be followed by a hands-on working session.
Locally linear reconstruction based missing value imputation for supervised learning
(2013)

Most learning algorithms generally assume that data is complete so each attribute of all instances is filled with a valid value. However, missing values are very common in real datasets for various reasons. In this paper, we propose a new single imputation method based on locally linear reconstruction (LLR) that improves the prediction performance of supervised learning (classification & regression) with various missing ratios. Next, we compare the proposed missing value imputation method (LLR) with six well-known single imputation methods for five different learning algorithms based on 13 classification and nine regression datasets. The experimental results showed that (1) all imputation methods helped to improve the prediction accuracy, although some were very simple; (2) the proposed LLR imputation method enhanced the modeling performance more than all other imputation methods, irrespective of the learning algorithms and the missing ratios; and (3) LLR was outstanding when the missing ratio was relatively high and its prediction accuracy was similar to that of the complete dataset. & 2013 Elsevier B.V. All rights reserved.